We summarize the process from data definition to distilled relationship as correlation, since it describes the creation and transformation of the relationships as a raw mathematical object, still uninformative to the human. The basis of correlation is determined by the unobserved population \(\mathcal {X}\) , sometimes called ground truth in machine learning (not to confuse with ground truth label), from where we sample a dataset \(X\) . Further, we induce a model \(M\)  of the model-class \(\mathcal {M}\)  to extract correlation from the dataset. Depending on our question we want to make a causal statement about, we have to design a query \(Q\)  from a query-class \(\mathcal {Q}\) , which describes the interpretable mechanism we either apply as a method upon our model \(M\)  or implement as an inductive bias into it. \(Q\)  acts either on a single instance \(x \in X\)  for a local explanation or on \(X\)  for a global. In application, \(\mathcal {Q}\)  is defined as a subgroup of interpretable models or explanatory methods solving for the same question, such as, e.g., attribution methods solving for feature importance [1]}, [2]}, [3]}. \(Q\)  is then defined as the specific method, e.g., Integrated Gradients [1]}. The design of the query is crucial in the sense that it has to be faithful to the model and useful (sometimes also called comprehensible) to the human [5]}. Otherwise, the induced bias by the query can lead to social misalignment [6]} (see fig2, right images from [7]}), “[...] a situation where an explanation communicates a different kind of information than the kind that people expect it to communicate.” [8]}.
